Astrometric Redshifts of Supernovae
Jaemyoung Jason Lee, Masao Sako, Richard Kessler, Alex I. Malz, and, The LSST Dark Energy Science Collaboration

TL;DR
This paper proposes using Differential Chromatic Refraction effects in multi-band imaging to estimate supernova redshifts, demonstrating improved accuracy when combined with other photometric methods, especially for large surveys like LSST.
Contribution
The study introduces a novel method leveraging DCR for supernova redshift estimation and shows its effectiveness in simulated LSST data, enhancing redshift accuracy.
Findings
Astrometric redshifts are accurate at z < 0.6 with 5-mas systematic uncertainty.
Combining astrometric, host galaxy, and light-curve redshifts improves overall accuracy.
DCR-based redshifts are valuable for large surveys lacking spectroscopic data.
Abstract
Differential Chromatic Refraction (DCR) is caused by the wavelength dependence of our atmosphere's refractive index, which shifts the apparent positions of stars and galaxies and distorts their shapes depending on their spectral energy distributions (SEDs). While this effect is typically mitigated and corrected for in imaging observations, we investigate how DCR can instead be used to our advantage to infer the redshifts of supernovae from multi-band, time-series imaging data. We simulate Type Ia supernovae (SNe Ia) in the proposed Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Deep Drilling Field (DDF), and evaluate astrometric redshifts. We find that the redshift accuracy improves dramatically with the statistical quality of the astrometric measurements as well as with the accuracy of the astrometric solution. For a conservative choice of a 5-mas systematic…
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Taxonomy
TopicsGamma-ray bursts and supernovae · Astronomy and Astrophysical Research · Stellar, planetary, and galactic studies
